With the development of graph and image information technology, it is more and more common to provide information at a website through an image or a picture, which is as popular as it is intuitive and convenient and which provides high volume information. Compared with traditional text, the technology for presenting graphs and images at a website is more difficult. Due to a specialty of the website and its business type, not every image with any format or any attribute (or unspecific image) may comply with applicable requirements of the website. Thus, before or after the image is applied at the website, the image often needs detecting to ensure it is a qualified image.
The conventional techniques mainly use two methods to detect a specific image that is applicable at the website. One method is an automatic detecting method that is directed to basic information of the specific image. The other method is a manual detecting method with respect to complex information of the specific image. The former method, for example, reads information such as heights and widths of images from an image database and calculates other basic information such as a ratio of the height to width. As the applications of the website are more and more complicated and diversified, the simple automatic detecting method for the basic information, although satisfying a requirement of efficiency, fails to meet a requirement of detecting complicated information. The latter method, after analyzing the images in the database, obtains complicated information such as an existence of a frame of the image, a region of a principal content, and a region of a background. However, when there is a massive amount of images applicable at the website, the manual detecting method cannot satisfy such requirements. Thus, the conventional techniques cannot satisfy the requirement of applying the specific images at the website.